Automated vehicles (AVs) are believed to have great potential to improve the traffic capacity and efficiency of the current transport systems. Despite positive findings of the impact of AVs on traffic flow and potential road capacity increase for highways, few studies have been performed regarding the impact of AVs on urban roads. Moreover, studies considering traffic volume increase with a mixture of AVs and human-driven vehicles (HDVs) have rarely been conducted. Therefore, this study investigated the impact of gradual increments of AV penetration and traffic volume on urban roads. The study adopted a microsimulation approach using VISSIM with a Wiedmann 74 model for car-following behavior. Parameters for AVs were set at the SAE level 4 of automation. A real road network was chosen for the simulation having 13 intersections in a total distance of 4.5 km. The road network had various numbers of lanes from a single lane to five lanes in one direction. The network consists of a main arterial road and a parallel road serving nearby commercial and residential blocks. In total, 36 scenarios were investigated by a combination of AV penetrations and an increase in traffic volumes. The study found that, as AV penetration increased, traffic flow also improved, with a reduction of the average delay time of up to 31%. Also, as expected, links with three or four lanes had a more significant impact on the delay. In terms of road capacity increase, when the penetration of AVs was saturated at 100%, the road network could accommodate 40% more traffic.

It is common to call a taxi by taxi-apps in Korea and it was believed that an app-taxi service would provide customers with more convenience. However, customers’ requests can often be denied, as taxi drivers can decide whether to take calls from customers or not. Therefore, studies on factors that determine whether taxi drivers refuse or accept calls from customers are needed. This study investigated why taxi drivers might refuse calls from customers and factors that influence the success of matching within the service. This study used origin-destination data in Seoul and Daejeon obtained from T-map Taxis, which was analyzed via a decision tree using machine learning. Cross-validation was also performed. Results showed that distance, socio-economic features, and land uses affected matching success rate. Furthermore, distance was the most important factor in both Seoul and Daejeon. The matching success rate in Seoul was lowest for trips shorter than the average at midnight. In Daejeon, the rate was lowest when the calls were made for trips either shorter or longer than the average distance. This study showed that the matching success for ride-hailing services can be differentiated particularly by the distance of the requested trip depending on the size of the city.

New towns often experience a structural transit gap in early stages, where transport supply lags behind population growth. Demand-responsive transit (DRT) has emerged as a promising complementary solution; however, most studies rely on MNL-based ICLV models that do not account for error covariance across alternatives. This study applies an ICLV model, integrating an Error Component Mixed Logit kernel with latent variables, to analyze mode choice behavior in transit-poor new towns. Based on an SP-off-RP survey of 644 residents in new towns, 2576 observations were analyzed. The model incorporates five latent variables, including Transit Dissatisfaction, Convenience, Safety, Travel Time, and Travel Companion Sensitivity, and captures unobserved correlations through a two-level nesting structure. Results show that DRT has a significantly positive alternative-specific constant, indicating latent acceptance beyond observable attributes. DRT adoption is more common among transit-poor new town residents and highly educated individuals, but less common among car owners. Users are more sensitive to access and waiting time than to in-vehicle time. Convenience, Safety, and Travel Time significantly influence DRT utility, while Travel Companion Sensitivity reveals heterogeneous effects across modes. These findings provide behavioral insights for designing effective DRT strategies in transit-poor new towns.